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Uterine sarcomas have very poor prognoses and are sometimes difficult to distinguish from uterine leiomyomas on preoperative examinations. Herein, we investigated whether deep neural network (DNN) models can improve the accuracy of preoperative MRI-based diagnosis in patients with uterine sarcomas. Fifteen sequences of MRI for patients (uterine sarcoma group: n = 63; uterine leiomyoma: n = 200) were used to train the models. Six radiologists (three specialists, three practitioners) interpreted the same images for validation. The most important individual sequences for diagnosis were axial T2-weighted imaging (T2WI), sagittal T2WI, and diffusion-weighted imaging. These sequences also represented the most accurate combination (accuracy: 91.3%), achieving diagnostic ability comparable to that of specialists (accuracy: 88.3%) and superior to that of practitioners (accuracy: 80.1%). Moreover, radiologists’ diagnostic accuracy improved when provided with DNN results (specialists: 89.6%; practitioners: 92.3%). Our DNN models are valuable to improve diagnostic accuracy, especially in filling the gap of clinical skills between interpreters. This method can be a universal model for the use of deep learning in the diagnostic imaging of rare tumors.

Uterine inversion is a rare puerperal event in the third stage of labor. Nonpuerperal uterine inversion is even rarer and is mainly caused by uterine fibroids, uterine sarcoma, or endometrial cancer. This is the first report of uterine inversion caused by cervical cancer. A 67-year-old woman presented with a 10 cm pelvic mass. Contrast-enhanced magnetic resonance imaging revealed uterine inversion, which was preoperatively diagnosed to be caused by endometrial cancer and was treated using an extended abdominal hysterectomy. Postoperative histopathological examination revealed that the primary tumor was a squamous cell carcinoma with coexistent high-grade squamous intraepithelial lesions and small-cell neuroendocrine carcinoma. Immunostaining was diffusely positive for p16 and negative for estrogen receptors. The postoperative diagnosis was cervical squamous cell carcinoma. Our observations suggested that cervical carcinoma can cause uterine inversion by invading the corpus.

TonB-dependent signal transduction is a versatile mechanism observed in gram-negative bacteria, integrating energy-dependent substrate transport with signal relay. In Escherichia coli, the TonB-ExbBD motor complex energizes the TonB-dependent transporter FecA, facilitating ferric citrate import. FecA also functions as a sensor, transmitting signals to the cytoplasmic membrane protein FecR. We previously demonstrated that FecR undergoes a three-step cleavage process, culminating in the activation of the cytoplasmic sigma factor FecI, which drives fec gene transcription. Here, we describe the complete mechanism of FecR cleavage-mediated ferric citrate signaling involving FecA and TonB. The cleavage cascade begins with FecR autoproteolysis prior to membrane integration. The soluble C-terminal domain (CTD) fragment of FecR is co-translocated with the N-terminal domain (NTD) fragment through a Tat system-mediated process. In the periplasm, the interaction between the CTD and NTD fragments prevents further cleavage. This inhibition is lifted by TonB-mediated motor function, which releases the CTD, allowing the cleavage cascade to proceed. This process is essential for ferric citrate signal-induced activation of fec gene expression. Our findings reveal that the regulation of FecR cleavage, relying on the TonB-FecA axis, plays a central role in bacterial response to ferric citrate signals.Competing Interest StatementThe authors have declared no competing interest.